Does DMLC XGBoost have any method (Grid search, Bayesian or Random Search, etc) to choose the best values of hyperparameters to be used?
The XGBoost from Sklearn has it: Nested versus non-nested cross-validation — scikit-learn 0.24.1 documentation (scikit-learn.org)
But I was not able to use its API with DMLC XGBoost because DMLC uses xgb.train(…) instead of xgb.fit(…).
How can I tune the hyperparameters for DMLC XGBoost?
Please help. Thank you.